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The FloCHANNEL is intended for use during sleep disorder studies to detect respiratory airflow onto a physiological recorder. A filtered disposable nasal/oral cannula attaches to the patient and connects into the input of the FloCHANNEL device. The outputs of the device provide low-voltage signals that are intended to be input into a physiological recorder.

The FloCHANNEL device can be used for pediatric patients, two years and older and adult patients including geriatric patients. The device is not intended for pediatrics and infants below two years of age for the purpose of respiration or SIDS monitoring.

The FloCHANNEL, FC-100, Sleep Diagnostic Device accomplishes its intended purpose by providing a measure nasal airflow resistance effects. The device simultaneously measures relative airflow of isolated left, right, and oral passages. The FloCHANNEL, FC-100, provides analog voltage out hardware amplified analog voltages from three all now sensors, hardware analog voltages to aid in scoring the respiration of a patient. The outputs provided an have a summed output, and two microprocessor all now sensors, hardware analog voltages to aid in scoring the respiration of a patient.

The FloCHANNEL, FC-100, is dependent on the patient interface unit to be able to compute and detect the respiration patterns of the patient. This accessory is three air compute and delove the respirately pannula. The cannula is of one-piece construction that allows independent pneumatic fluid connection between the FloCHANNEL, FC-100, that the patient's left nasal airway, right nasal airway, and oral airway. The filter shall arra the patient of on habat all ways greater than or equal to 0.1 microns in size between prevent the transfer or partiountios groater and filter per channel on the patient interface unit with independently sealed chambers to prevent cross flow between pneumatic channels.

The FloCHANNEL has an output selection switch that is accessible from the outside of the enclosure and to a lour pockibility in the output options to be able to configure to a the switch to to ano it signal conditioners. With this switch there is the ability to create four different output voltage ranges.

There are six (6) output connectors on the FloCHANNEL, FC-100. All output connectors are Nort Julier. This re Nasal Processed Airflow, Oral Airway Processed Airflow, Sum r 10003500 Airflow and Right Nasal Processed Airflow, Relative Tidal Volume, and IE Marker.

The provided text does not contain specific acceptance criteria or a detailed study report with performance metrics. The document is a 510(k) summary for the Dormio Tech FloCHANNEL Model FC-100, focusing on demonstrating substantial equivalence to a predicate device.

However, I can extract the information requested as much as possible based on the available text, and note where information is not present.

Acceptance Criteria and Device Performance Study in 510(k) Summary for Dormio Tech FloCHANNEL Model FC-100

The provided 510(k) summary for the Dormio Tech FloCHANNEL Model FC-100 states that "The FloCHANNEL passed all of the tests and acceptance criteria as outlined in the test procedures. Test data demonstrates equivalence to the predicate device." However, the document does not explicitly define the specific acceptance criteria or provide the detailed reported device performance against those criteria. It focuses on claiming substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: The document does not provide quantitative acceptance criteria (e.g., specific accuracy thresholds, signal-to-noise ratios, or other performance specifications) nor does it provide quantitative performance data (e.g., specific numerical results from tests).

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. The document states "All risk assessment, design verification and validation activities were conducted in accordance with the device product requirements" and "The FloCHANNEL was proven clinically equivalent or superior to the predicate device." This suggests validation data was collected, but details on the nature (retrospective/prospective, country of origin, etc.) are absent.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• MRMC Study: Not mentioned. The submission focuses on device equivalence, not human reader improvement with AI assistance. The device is a "Breathing Frequency Monitor" (ventilatory effort recorder), which typically involves direct physiological measurement rather than image interpretation by human readers.

6. Standalone Performance Study (Algorithm Only)

• Standalone Study: The device itself is a standalone hardware device that provides analog voltage outputs. "Standalone performance" in the context of an algorithm or AI is not applicable here as the device is a sensor and signal conditioner for direct physiological measurement, without embedded AI for interpretation. It outputs raw data ("low-voltage signals") to a physiological recorder for subsequent analysis by other systems or human experts.

7. Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated. For a breathing frequency monitor, ground truth would typically refer to highly accurate reference measurements of airflow or ventilatory effort, likely from established medical equipment or gold-standard methodologies, which the device's output signals are compared against.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This device is a hardware sensor and signal conditioner, not a machine learning model that requires a training set.

9. How Ground Truth for the Training Set was Established

• Ground Truth for Training Set: Not applicable, as there is no training set for this type of device.

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The SleepCheck is intended for use in monitoring nasal and oral airflow. The device is intended for use as a prescreening tool to determine the need for clinical diagnosis and evaluation by polysomnography based on the patient's test score.
The SleepCheck is a small monitor designed to assess nasal and oral airflow. Apnea breathing events are counted based on a reduction in airflow. The device is intended for use as a screening device to determine the need for clinical diagnosis and evaluation by polysomnography based on the patient's score.

The SleepCheck is a pager-sized monitor that clips onto the patient's nightshirt. The monitor utilizes an air cannula sensor to capture the patient's nasal and oral airflow. The sensor cannula, containing 3 airflow ports, is placed under the nostrils, above the lip and held in place around the ears with a lanyard. The unit has an LCD readout that displays the total number of apneas and the apnealhypopnea index (AHI). The LCD also flashes a warning if the sensor is improperly applied or displaced. The LCD also provides a dynamic bar graph that displays the breathing pattern. As the patient inhales and exhales, the bar moves back and forth, verifying proper sensor application and monitor operation. When breathing decreases or is interrupted for 10 seconds or longer, this event is considered an apnea episode and is tallied on the LCD readout. The patient self-applies the unit before going to bed and wears the unit through the night. Upon waking, the patient will take off the unit and return it to the physician. The physician will check the LCD to learn the number of apneas and the rate of apnea events (AHI) that occurred throughout the night. The technical validity of the test is also indicated.

Here's a breakdown of the acceptance criteria and study information for the SleepCheck device, based on the provided text:

Acceptance Criteria and Device Performance

Note: The document only explicitly states "high sensitivity and specificity" as a general goal within the "Assessment of SleepCheck Performance Data" section. The predicate device's performance (SleepStrip) serves as an implicit benchmark for what constitutes an acceptable level of performance in the market. The SleepCheck's performance significantly exceeds that of the predicate device for all reported metrics, suggesting it meets or exceeds an implicit acceptance standard.

Study Information

2. Sample Size Used for the Test Set and Data Provenance:

• Sample Size: 111 sleep tests, totaling 151 sleep nights.
• Data Provenance: Not explicitly stated whether retrospective or prospective, or country of origin for the patient data. However, the studies were "sponsored by the National Institutes of Health" and conducted "both at-home and in the sleep laboratory," suggesting a U.S.-based context and potentially a mix of prospective and retrospective elements depending on study design.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set:

• Not specified.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• No, a MRMC comparative effectiveness study was not conducted with human readers comparing performance with and without AI assistance. The study focuses on the standalone performance of the device against a gold standard.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance:

• Yes, a standalone study was done. The reported performance metrics (Sensitivity, Specificity, r to PSG) are for the "SleepCheck" device directly, indicating its algorithmic performance in detecting apnea events. The physician only reads the LCD display of the device's calculated results.

7. Type of Ground Truth Used:

• Polysomnography (PSG): PSG is explicitly stated as the "gold standard" against which the SleepCheck was validated.

8. Sample Size for the Training Set:

• Not specified. The document describes clinical studies used for validation, but does not differentiate or provide details on any specific training set for the algorithm's development.

9. How Ground Truth for the Training Set Was Established:

• Not specified. Assuming typical device development, ground truth for any training would also be established through expert interpretation of PSG data, similar to the validation set. However, this is not detailed in the provided text.

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